Graph-Augmented Co-Attention Model for Socio-Sequential Recommendation

Abstract

A sequential recommendation has become a hot research topic, which seeks to predict the next interesting item for each user based on his action sequence. While previous methods have made many efforts to capture the dynamics of sequential patterns, we contend that they still suffer from two inherent limitations: 1) they fail to model item transition patterns in an efficient and time-sensitive manner and 2) they are unaware of the importance of dynamically capturing social influence, resulting in suboptimal performance. We introduce a new concept dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">socio-sequential recommendation</i> , where the challenge mainly lies in dynamically modeling social influences and capturing item-to-item transition patterns in a time-sensitive manner. In light of this, we contribute a novel solution named GCARec (short for graph-augmented co-attention model), which takes into account the joint effect of dynamic sequential patterns and dynamic social influences. GCARec decomposes socio-sequential recommendation workflow into two steps. First, we adopt a light graph embedding module to model long-term user preference. Then, we propose a time-sensitive attention mechanism and a social-aware attention mechanism to capture dynamic patterns at sequential-level and social-level, respectively. Extensive experiments have been conducted on eight real-world datasets from different scenarios, demonstrating the superiority of GCARec against several state-of-the-art methods. The codes and datasets have been released at: https://github.com/wubinzzu/GCARec.